SOTAVerified

Autonomous Vehicles

Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1951–1975 of 2605 papers

TitleStatusHype
Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Global Image Segmentation Process using Machine Learning algorithm & Convolution Neural Network method for Self- Driving Vehicles—0
A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge—0
Keep your Eyes on the Lane: Real-time Attention-guided Lane DetectionCode1
Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection—0
Trajectory Prediction using Equivariant Continuous Convolution—0
A Lane Merge Coordination Model for a V2X Scenario—0
Pedestrian Intention Prediction: A Multi-task PerspectiveCode1
RONELD: Robust Neural Network Output Enhancement for Active Lane DetectionCode1
Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles—0
Transportation Internet: Concepts, Models, and Architectures—0
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation—0
Neural circuit policies enabling auditable autonomyCode1
Audio-Visual Self-Supervised Terrain Type Discovery for Mobile Platforms—0
A Cooperative Control Framework for CAV Lane Change in a Mixed Traffic Environment—0
Using Empirical Trajectory Data to Design Connected Autonomous Vehicle Controllers for Traffic Stabilization—0
Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion—0
Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations—0
Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation LearningCode1
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign RecognitionCode0
Learning to Localise Automated Vehicles in Challenging Environments using Inertial Navigation Systems (INS)—0
Automotive Radar Data Acquisition using Object DetectionCode0
6G Cellular Networks and Connected Autonomous Vehicles—0
Facilitating Connected Autonomous Vehicle Operations Using Space-weighted Information Fusion and Deep Reinforcement Learning Based Control—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BAAMA3DP22.85—Unverified
2GSNetA3DP20.21—Unverified